TY - GEN
T1 - A Hybrid Information Fusion Method for Fusing Data Extracted from Inspection Reports for Supporting Bridge Data Analytics
AU - Liu, Kaijian
AU - El-Gohary, Nora
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - There has been an increasing demand for data-driven and machine learning-based bridge deterioration prediction approaches for supporting enhanced bridge maintenance decision making. Bridge inspection reports, which contain a wealth of information about bridge conditions, open opportunities for data analytics to better understand and predict bridge deterioration. However, learning from the reports is challenging, because they usually contain multiple - even ambiguous, uncertain, and conflicting - information about the same bridge element, its deficiencies, and its deficiency measurements. Learning from such data negatively affects the generalizability and the separability of machine learning models, which compromises the performance of data-driven prediction. To address this challenge, this paper proposes a hybrid information fusion method. The method includes two main components: named entity normalization for fusing concepts in ambiguous surface forms into a canonical form, and data fusion for fusing numerical deficiency measurements containing uncertainties and conflicts into a unified and consistent representation. This paper focuses on analyzing the data fusion requirements, and presenting the proposed data fusion method and its evaluation results. The results indicate that the proposed method can adequately address the fusion requirements.
AB - There has been an increasing demand for data-driven and machine learning-based bridge deterioration prediction approaches for supporting enhanced bridge maintenance decision making. Bridge inspection reports, which contain a wealth of information about bridge conditions, open opportunities for data analytics to better understand and predict bridge deterioration. However, learning from the reports is challenging, because they usually contain multiple - even ambiguous, uncertain, and conflicting - information about the same bridge element, its deficiencies, and its deficiency measurements. Learning from such data negatively affects the generalizability and the separability of machine learning models, which compromises the performance of data-driven prediction. To address this challenge, this paper proposes a hybrid information fusion method. The method includes two main components: named entity normalization for fusing concepts in ambiguous surface forms into a canonical form, and data fusion for fusing numerical deficiency measurements containing uncertainties and conflicts into a unified and consistent representation. This paper focuses on analyzing the data fusion requirements, and presenting the proposed data fusion method and its evaluation results. The results indicate that the proposed method can adequately address the fusion requirements.
UR - http://www.scopus.com/inward/record.url?scp=85068736491&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068736491&partnerID=8YFLogxK
U2 - 10.1061/9780784482445.014
DO - 10.1061/9780784482445.014
M3 - Conference contribution
AN - SCOPUS:85068736491
T3 - Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 105
EP - 112
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -